In a physiologically normal state, the mitral valve maintains unidirectional blood flow across the left heart, and its geometry and mechanics are essential to proper cardiac function. The valve consists of two leaflets (anterior and posterior), a fibro-elastic ring (the annulus) which anchors the leaflets to the surrounding heart tissue, and a subvalvular apparatus comprised of chordae tendinae and papillary muscles that synchronize mitral leaflet, annular, and left ventricular wall motion.
Three dimensional transesophageal echocardiography (3D TEE) has been effectively used in both research and clinical settings to visualize and quantify mitral valve morphology and motion in vivo (Abraham, T. P., et al., “Feasibility, accuracy, and incremental value of intraoperative three-dimensional transesophageal echocardiography in valve surgery,” Am J Cardiol, Vol. 80, pp. 1577-1582 (1997); Ahmed, S., et al., “Usefulness of transesophageal three-dimensional echocardiography in the identification of individual segment/scallop prolapse of the mitral valve,” Echocardiography, Vol. 20, pp. 203-209 (2003); Grewal, J., et al., “Real-time three-dimensional transesophageal echocardiography in the intraoperative assessment of mitral valve disease,”. J Am Soc Echocardiogr, Vol. 22, pp. 34-41 (2009); Sugeng, L., et al., “Live 3-dimensional transesophageal echocardiography initial experience using the fully-sampled matrix array probe,” J Am Coll Cardiol, Vol. 52, pp. 446-449 (2008); Vergnat, M., et al., “Ischemic mitral regurgitation: a quantitative three-dimensional echocardiographic analysis,” Ann Thorac Surg, Vol. 91, pp. 157-164 (2011); Veronesi, F., et al., “Semi-automatic tracking for mitral annulus dynamic analysis using real-time 3D echocardiography,” Computers in Cardiology, Vol. 33, pp. 113-116 (2006); and Wei, J., et al., “The routine use of live three-dimensional transesophageal echocardiography in mitral valve surgery: clinical experience,” Eur J Echocardiogr, Vol. 11, pp. 14-18 (2010)). Comprehensive evaluation of 3D valve morphology is essential for the diagnosis and surgical treatment of many valvular heart diseases, especially those associated with complex morphological abnormalities. Ischemic mitral regurgitation, in particular, manifests as a variable combination of distortions in valve geometry: annular dilatation and apical leaflet tethering. These distortions are patient-specific and underscore distinct pathophysiologic mechanisms and abnormalities.
3D examination of patient-specific valve anatomy is a pre-requisite for disease characterization and selection of appropriate surgical treatment strategies. 3D TEE has been demonstrated to be a valuable tool in preoperative surgical planning (Garcia-Orta, R. et al., “Three-Dimensional versus two-dimensional transesophageal echocardiography in mitral valve repair, J. Am. Soc. Echocardiogr., Vol. 20, pp. 4-12 (2007)), intraoperative guidance (Eng, M. H., et al., “Implementation of real-time three-dimensional transesophageal echocardiography in percutaneous mitral balloon valvuloplasty and structural heart disease interventions,” Echocardiography, Vol. 26, pp. 958-966 (2009); Swaans, M. J., et al., “Three-dimensional transesophageal echocardiography in a patient undergoing percutaneous mitral valve repair using the edge-to-edge clip technique,” Eur J Echocardiogr, Vol. 10, pp. 982-983 (2009)), and immediate and long-term follow-up to determine the need for further cardiological surgical intervention (De Castro, S., et al., “Qualitative and quantitative evaluation of mitral valve morphology by intraoperative volume-rendered three-dimensional echocardiography,” J. Heart Valve Dis., Vol. 11, pp. 173-180 (2002)). However, the limitation of the current commercial 3D TEE imaging platforms is that they provide visually impressive 3DE image volume renderings, but enable only a limited number of quantitative measurements to be made off-line with somewhat cumbersome user interaction. The existing 3D TEE image analysis tools are therefore impractical and inadequate for use in quantitative image-based surgical planning.
To increase the practicality and ease of mitral valve quantification with 3D TEE, several semi-automatic and one fully automatic method for mitral leaflet segmentation have been proposed by R. I. Ionasec, et al., “Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE,” IEEE Trans Med Imaging, vol. 29, pp. 1636-51, September 2010; P. Burlina, et al., “Patient-specific modeling and analysis of the mitral valve using 3D-TEE,” in Lecture Notes in Computer Science. vol. 6135, ed, 2010, pp. 135-146; Pouch, A. M., et al., “Development of a semi-automated method for mitral valve modeling with medial axis representation using 3D ultrasound,” Med Phys, Vol. 39, pp. 933-950 (2012); and Schneider, R. J., et al., “Modeling mitral valve leaflets from three-dimensional ultrasound,” Lecture Notes in Computer Science, Springer-Verlag, pp. 215-222 (2011)). The goal of these techniques is to derive quantitative measurements and 3D visualizations of annular and leaflet geometry from 3D TEE images. The methods vary in the extent of requisite user interaction and the level of detail with which the mitral leaflets are represented.
FIG. 1 illustrates several of the challenges specific to mitral leaflet and annular segmentation in 3D TEE images. In FIG. 1, cross-sectional images of 3D TEE image volumes at diastole (left and center) and systole (right) illustrate the challenges specific to mitral leaflet segmentation. The top arrows points toward points on the annulus, showing that there is no image-based boundary between the mitral leaflets and adjacent tissue to which the leaflets are attached. The lower arrow in the left and center images points towards the posterior leaflet at diastole, which is often pressed against the ventricular wall and is characterized by signal dropout. The lower arrow in the right image points to the coaptation zone of the leaflets at systole, showing there is no intensity-based demarcation between the anterior and posterior leaflets. As shown in FIG. 1, there is no intensity-based boundary between the leaflets and adjacent heart tissue, making it difficult to identify the annulus and leaflet boundaries based in image intensity information alone. Also, the posterior leaflet often gets pressed against the left ventricular wall during diastole and is characterized by signal dropout, making it difficult for an automated segmentation algorithm to capture posterior leaflet geometry when the valve is open. In addition, the anterior and posterior leaflets are difficult to distinguish in the coaptation zone during systole since there is no intensity-based distinction between the two leaflets.
To address these challenges, Schneider and colleagues in Schneider, R. J., et al., “Patient-specific mitral leaflet segmentation from 4D ultrasound,” Med Image Comput Assist Interv, Vol. 14, pp. 520-527 (2011) present a multi-stage method for segmenting the open mitral leaflets in 3D TEE data sets, wherein the leaflets are represented by a discrete mesh. First, the mitral annulus is semi-automatically segmented as described by Schneider, R. J., et al., in “Mitral annulus segmentation from 3D ultrasound using graph cuts,” IEEE Trans Med Imaging, Vol. 29, pp. 1676-1687 (2010), and an initial leaflet search space is defined in the image volume. The search space is subsequently refined, and the leaflet surfaces are estimated using graph cut and active contour methods. This technique requires some minimal user interaction to generate patient-specific leaflet representations. Ionasec and colleagues describe a fully automatic technique for segmenting and tracking the aortic and mitral leaflets in computed tomography and 3D TEE data. Given a large database of manually labeled images, machine learning algorithms are used to globally locate and then track several valve landmarks throughout the cardiac cycle. Leaflet geometry is subsequently represented by a parametric model fitted through these points. While this method is fully automated and establishes correspondences, the use of sparse landmarks potentially limits patient-specific detail of leaflet geometry. The techniques described by Schneider et al. and Ionasec et al. both represent the mitral leaflets as a single surface, rather than structures with thickness. Alternatively, Burlina et al. use 3D active contours and thin tissue detection to recover mitral leaflet geometry at end-diastole in 3D TEE image data. While this method captures detail of leaflet geometry, it requires user initialization and manual refinement. Several other techniques, largely based on manual leaflet tracing in either custom or commercial software, have also been proposed (Vergnat et al. (2011); Tsukiji, M., et al., “3D quantitation of mitral valve coaptation by novel software system with transthoracic real-time 3D echocardiography,” Circulation, Vol. 114, pp. 716-717(2006); and Veronesi et al., (2006)). While these techniques provide spatially dense, expert-defined representations of leaflet and annular geometry, they are the most labor intensive methods.
Although automatic 3D quantification tools have significant implications for diagnostics and surgical care, the existing methodologies remain labor and time intensive. Methods that reduce inter-observer variability in 3D TEE image analysis would maximize its practicality for use at the bedside and in the operating room (Lang, R. M., and Adams, D. H., “3D echocardiographic quantification in functional mitral regurgitation,” JACC Cardiovasc Imaging, Vol. 5, pp. 346-347 (2012)). A goal of the present invention is to develop an alternative, leaflet segmentation method that is fully automated, captures patient-specific detail, represents the leaflets with finite thickness, and establishes correspondences on valves of different subjects. To accomplish these goals, the inventors propose a segmentation technique that integrates probabilistic segmentation and geometric modeling techniques. These complementary methods are multi-atlas joint label fusion and deformable modeling with continuous medial representation (cm-rep).
Cm-rep is a type of shape representation that describes an object in terms of its medial geometry, i.e. a radial thickness field mapped to a skeleton, or medial axis (Pizer, S. M., et al., “Deformable m-reps for 3D medical image segmentation,” International Journal of Computer Vision, Vol. 55, pp. 85-106 (2003); and Yushkevich, P. A., et al., “Continuous medial representation for anatomical structures,” IEEE Trans Med Imaging, Vol. 25, pp. 1547-1564 (2006)). The representation has been used to model various anatomical shapes, including the hippocampus (Yushkevich, P. A., “Continuous medial representation of brain structures using the biharmonic PDE,” Neuroimage, Vol. 45, pp. S99-110 (2009)) and cardiac ventricles (Sun, H., et al., “Automatic cardiac MRI segmentation using a biventricular deformable medial model,” Med Image Comput Assist Interv, Vol. 13, pp. 468-475 (2010)), and is especially useful for modeling thin, sheet-like structures. The inventors have previously shown that cm-rep is an appropriate shape model for describing mitral leaflet and annular geometry (Pouch et al., 2012). It establishes correspondences on different valve shapes and facilitates measurement of clinically relevant features of annular and leaflet geometry. In earlier work by the present inventors, the cm-rep of a given valve shape is obtained by deforming a pre-defined template by Bayesian optimization to match a user-initialized segmentation of the leaflets (Pouch, A. M., et al., “Semi-automated mitral valve morphometry and computational stress analysis using 3D ultrasound,”J Biomech, Vol. 45, pp. 903-907 (2012); Pouch et al., 2012). This user-initialized segmentation method, based on 3D active contours with region competition, requires multiple interactive steps to identify valve location in the image volume, establish boundaries between the leaflets and adjacent heart tissue, and estimate a threshold to guide region competition. In the present invention, the need for user initialization is completely eliminated with the use of multi-atlas joint label fusion to generate probabilistic segmentations that guide model fitting.
Given a target image to segment, multi-atlas joint label fusion registers a set of manually labeled atlases of the mitral leaflets to the target image and propagates the segmentation labels to this target image. Joint label fusion assigns weights to the labels of different atlases based on the similarity between the atlas and target image, as well as the similarity between different atlases (Wang, H. Z., et al., “Multi-Atlas Segmentation with Joint Label Fusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, pp. 611-623 (2013). Although label fusion alone can generate segmentations of the mitral leaflets, the technique does not preserve leaflet topology or assign correspondences to different valve shapes. A method is desired that overcomes these challenges and the other challenges note above.